Porting Neural Ordinary Differential Equations (NeuralODEs), the combination of an artificial neural network and an ODE solver, to real engineering applications is still a challenging venture. However, we will show that Neural Functional Mock-up Units (NeuralFMUs), an evolved subgroup of NeuralODEs that contain Functional Mock-up Units (FMUs), are able to cope with these challenges. This paper briefly introduces to the topics NeuralODE and NeuralFMU and describes the procedure and considerations to apply this technique to a real engineering use case. Further, different workflows to apply NeuralFMUs dependent on tool capabilities and use case requirements are discussed. The presented method is illustrated with the creation of a Hybrid Twin of an hydraulic excavator arm, which has various challenges such as discontinuity, nonlinearity, oscillations and characteristic maps. Finally we will show, that the created Hybrid Twin, on basis of measurement data from a real system, gives more accurate results compared to a conventional simulation model based on first principles.

This work introduces a new solution for the modeling and simulation of dynamically constrained objects for limited structurally variable systems purely in Modelica. A combination of a collision detection algorithm, the limitation of collisions, and a method to constrain objects based on forces leads to a constraint network in Modelica. It allows a stable and accurate simulation of applications such as robot tool changers in a flexible way without the need for predefined connections in the model.

Computer simulation has become a vital tool for modeling complex systems.

However, the development and deployment of simulation models often involve multiple stages, tools, and teams, which can lead to significant challenges in maintaining quality, reliability, and efficiency.

DevOps, a set of practices that combines software development and IT operations, has emerged as a promising approach to streamline the simulation development.

However, most system engineers are not DevOps specialists and there are a lot of manual steps involved when writing build pipelines and

configurations of simulations.

For this purpose, an abstract graph-based meta-data model was presented in previous work to provide an automation framework for DevOps with simulations. In this work we want to continue our investigations

by expanding and harmonizing this approach to better work with established standards like SSP, SysML2 and DCP and demonstrating it's application on real-life use cases.

This paper introduces a new method for mechanical systems with its own interface that enables the object-oriented formulation of very stiff contacts. It thereby suppresses high frequencies and yields stable replacement dynamics leading to an equivalent steady state. Potential applications are the efficient modeling and simulation of robotic manipulation or the easier handling of what formerly have been variable-structure systems.